使用IFSQSARSAR识别物理化学性质估计中的不确定性
Trevor N Brown1, Alessandro Sangion2, Jon A Arnot2,3,4
1ARC Arnot Research & Consulting, Toronto, ON, M4C 2B4, Canada. trevor.n.brown@gmail.com.
Journal of cheminformatics
|May 30, 2024
概括
新的定量结构-活性关系 (QSAR) 模型预测了新化学品的关键物理化学性质. 这些模型被整合到IFSQSAR包中,为化学危险和风险评估提供了宝贵的见解.
科学领域:
- 计算化学和化学信息学.
- 开发化学性质的预测模型.
- 环境科学和风险评估.
背景情况:
- 准确预测物理化学 (PC) 属性对于化学危害,暴露和风险估计至关重要.
- 现有模型可能缺乏对新型,数据较差的化学品的强有力的验证.
- 定量结构-活动关系 (QSARs) 为财产预测提供了一个强大的方法.
研究的目的:
- 开发和评估六种新的可溶性,蒸汽压力和分离比率 (KOW,KOA,KAW) 的预测模型.
- 为了在 Python 软件包中实现这些模型,Iterative Fragment Selection 量化结构-活动关系 (IFSQSAR).
- 使用外部数据集评估模型对新化学品的预测性.
主要方法:
- 开发多参数线性自由能量关系 (PPLFER) 方程,整合实验数据和QSPR预测的描述符.
- 对摩尔体积和物理状态分类器实施辅助QSPR.
- 利用IFSQSAR方法用于适用性领域的表征和不确定性估计 (95%的预测间隔).
- 使用广泛的外部数据集测量分区比,蒸汽压力和可溶性进行验证.
主要成果:
- 现在IFSQSAR包 (v1.1.0) 包含了六种新的PC属性预测模型.
- 模型显示,新化学品的RMSEP为0.71.4的分区比 (日志KOW,日志KAW,日志KOA) 的预测性很好.
- 对蒸汽压力和溶解度的预测表现出更高的不确定性,受物理状态决定的影响.
- 需要1.25的缩放因子来预测分区比率的预测间隔,以涵盖95%的外部数据.
结论:
- 开发的PLFER-QSAR模型为预测新化学品的基本PC特性提供了一个强大的框架.
- IFSQSAR套件促进了实验数据和风险评估模型预测的无整合.
- 该研究强调了严格的外部验证和不确定性量化对于可靠的化学安全评估的重要性.
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